OP0025 FACTORS ASSOCIATED WITH FATIGUE AND ITS IMPROVEMENT – A PRINCIPAL COMPONENT ANALYSIS OF PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS FROM GUSELKUMAB PHASE 3 TRIALS
Authors: P. Rahman, P. J. Mease, A. Deodhar, A. Kavanaugh, S. D. Chakravarty, A. Kollmeier, Y. Liu, M. Shawi, C. Han
Journal: Annals of the Rheumatic Diseases (2022)

Background Fatigue, one of the top 3 patient (pt)-reported symptoms of psoriatic arthritis (PsA) and a recent PsA outcome domain, 1 causes impaired health-related quality-of-life, diminished productivity, and disability. 1-3 Although the origins of fatigue are multifactorial, inflammation is hypothesized to play an important role. 4 In pts with active PsA, treatment with guselkumab (GUS) led to clinically meaningful and sustained improvements in fatigue through 1 year in DISCOVER-1 (D1) and DISCOVER-2 (D2). 5 Objectives To identify 1) factors associated with fatigue and 2) factors associated with change in fatigue among pts with PsA treated with GUS. Methods In the Phase 3 D1 (N=381, biologic-naïve and tumor necrosis factor inhibitor-experienced) and D2 (N=739, biologic-naïve) studies, pts with active PsA despite standard therapies and/or biologic disease-modifying antirheumatic drugs were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO) with crossover to GUS 100 mg Q4W at W24. The pt-reported Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) scale measured fatigue (scored 0-52). In these post-hoc analyses of D1 and D2 pts, a principal component analysis (PCA) was performed using W0 data to identify the underlying baseline factors associated with fatigue. Additionally, linear regression analyses were performed to identify covariates associated with change in fatigue from W0 to W24. Results In 1120 pts (mean age 47 yrs, mean disease duration 5.9 yrs, 48% female), mean FACIT-Fatigue scores at baseline ranged from 29.1 to 31.4 (vs 43.6 for the general US population). 5 PCA showed that 62% of the variability in fatigue could be explained by 3 components (Figure 1). The first component, explaining 34% of variability in fatigue, largely comprised systemic disease activity and function measures such as pain, pt global assessment of disease activity (PtGDA), physician’s global assessment of disease activity, and Health Assessment Questionnaire-Disability Index (HAQ-DI). The second component, explaining 16% of variability, comprised joint manifestations including swollen joint count (SJC) and tender joint count (TJC). Skin involvement as assessed by Psoriasis Area and Severity Index (PASI) and systemic inflammation (C-reactive protein [CRP]) could explain 12% of the variability in fatigue (Figure 1 and Table 1). In a multivariate linear regression analysis, after adjusting for effects from other variables, improvement in CRP, physical function (HAQ-DI), PtGDA, and PASI score were significantly associated with fatigue improvement in GUS-treated pts at W24 (all p&lt;0.001). Table 1. PCA of Pts With Active PsA in D1+D2 (N=1120; Pooled W0 data): Factor Loading Estimates by Covariates Component1 Systemic Disease Activity and Function Component 2 Joint Manifestations Component 3 Skin Involvement and Inflammation PsA disease duration, yr 0.10 0.14 0.25 PASI total score (0-72) 0.22 0.23 0.74 CRP, mg/dL 0.36 -0.13 0.55 HAQ-DI score (0-3) 0.73 -0.09 -0.19 Pain (0-10 VAS) 0.83 -0.35 -0.13 PtGDA (0-10 VAS) 0.82 -0.36 -0.16 Physician global assessment of disease activity (0-10 VAS) 0.65 -0.18 0.23 SJC (0-66) 0.50 0.74 -0.12 TJC (0-68) 0.54 0.70 -0.18 VAS=Visual Analog Scale. Conclusion Among pts with PsA, measures of systemic disease activity and function, followed by joint manifestations, and skin involvement/inflammation accounted for 62% of the variability in fatigue. The large residual effect (38%) that was unexplained by the current model suggests the need for further research to identify additional factors (eg, distinct molecular pathways) contributing to the fatigue reported by PsA pts. References [1]Leung YY, et al. J Rheumatol (Suppl ). 2020;96:46-9. [2]Gudu T, et al. Joint Bone Spine . 2016;83:439-43. [3]Husted JA, et al. Ann Rheum Dis . 2009;68:1553-8. [4]Krajewska-Włodarczyk M, et al. Reumatologia . 2017;55:125-30. [5]Rahman P, et al. Arthritis Res Ther . 2021;23:190. Disclosure of Interests Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Aurinia, Bristol Myers Squibb, Celgene, Eli Lilly, GlaxoSmithKline, Janssen, MoonLake, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GlaxoSmithKline, Novartis, Pfizer, and UCB, Arthur Kavanaugh Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Genentech, Janssen, Merck, Novartis, Pfizer and UCB, Soumya D Chakravarty Shareholder of: Johnson &amp; Johnson, Employee of: Janssen Scientific Affairs, LLC, Alexa Kollmeier Shareholder of: Johnson &amp; Johnson, Employee of: Janssen Research &amp; Development, LLC, Yan Liu Shareholder of: 3 Johnson &amp; Johnson, Employee of: Janssen Research &amp; Development, LLC, May Shawi Shareholder of: Johnson &amp; Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson &amp; Johnson, Chenglong Han Shareholder of: Johnson &amp; Johnson, Employee of: Janssen Research &amp; Development, LLC.